Guest Editors
Dr. Mazin Abed Mohammed, University of Anbar, Iraq
Prof. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania
Prof. Valentina Emilia Balas, Aurel Vlaicu University of Arad, Romania
Prof. Aniello Castiglione, University of Naples Parthenope, Italy
Summary
Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment. Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.
Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.
Keywords
The topics of interest include, but are not limited to:
• Computational Intelligence methodologies for medical data analysis;
• Computational Intelligence and block chain assisted medical efficient product designs;
• Computational Intelligence for medical big data analysis;
• Computational Intelligence for medical decision support systems in Parkinson's disease;
• Computational Intelligence for medical decision support systems in heart disease;
• Computational Intelligence for medical decision support systems in cancers diagnostic;
• Advancements in deep learning algorithms in health informatics;
• Computational Intelligence for wearable medical devices;
• Computational Intelligence management in IoMT devices;
• Deep learning for data analytics in body sensor networks;
• Machine learning applied to Healthcare Systems;
• Medical image recognition using AI technologies;
• Machine and deep learning approaches based observation in case of COVID-19;
• Computational methods for COVID-19 prediction and detection;
• Data mining and knowledge discovery in healthcare;
• COVID-19 analysis using Big Data;
• Medical Management system for COVID-19;
• Big Data Analytics for prediction and application for COVID-19;
• AI Methodologies;
• Soft Computing approaches;
• Optimizations methods in complex problems;
• Big Data Analytics for Wireless area network.
Published Papers
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